Experimentally and computationally validated ml articles that use more than 10000 training datapoints are sparse compared to the previous two training datapoint set sizes considered in this work. This is to be expected given the difficulty of generating a reliable dataset of this magnitude, either experimental or computational. This problem is especially exacerbated in materials-related projects as many synthesis methods are lengthy and difficult to procure. preference towards ann may have been expected, given the limited number of articles, no clear trend emerges. We now present experimental [1]}, [2]}, [3]} and computational [4]}, [5]} examples: ann [2]}, [5]}, rf [1]}, dt [4]}, and bo [2]}, [3]}.
